Linked Life Data

17555247

Source:http://linkedlifedata.com/resource/pubmed/id/17555247

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2007-6-8
pubmed:abstractText
Polyp size is one important biomarker for the malignancy risk of a polyp. This paper presents an improved approach for colonic polyp segmentation and measurement on CT colonography images. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy clustering, and adaptive deformable model. Since polyps on haustral folds are the most difficult to be segmented, we propose a dual-distance algorithm to first identify voxels on the folds, and then introduce a counter-force to control the model evolution. We derive linear and volumetric measurements from the segmentation. The experiment was conducted on 395 patients with 83 polyps, of which 43 polyps were on haustral folds. The results were validated against manual measurement from the optical colonoscopy and the CT colonography. The paired t-test showed no significant difference, and the R(2) correlation was 0.61 for the linear measurement and 0.98 for the volumetric measurement. The mean Dice coefficient for volume overlap between automatic and manual segmentation was 0.752 (standard deviation 0.154).
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0094-2405
pubmed:author
pubmed:issnType
Print
pubmed:volume
34
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1655-64
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Adaptive deformable model for colonic polyp segmentation and measurement on CT colonography.
pubmed:affiliation
Diagnostic Radiology Department, Clinical Center National Institutes of Health, Bethesda, Maryland 20892, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Intramural